User authentication method based on keystroke dynamics and mouse dynamics using HDA

Shi, Yutong; Wang, Xiujuan; Zheng, Kan; Cao, Siwei

doi:10.1007/s00530-022-00997-5

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Other studies of these issues include that of Raul, Shankarmani, and Joshi [42], who proposed combining nonconventional features with the conventional time-based features for user identification in static KD using ML classifiers and observed improvements in the false reject rate (FRR), false accept rate (FAR), and EER; their five ML algorithms determined that the logistic regression method www.ijacsa.thesai.org achieved 90.50% accuracy. Shi, X. Wang, Zheng, and Cao [43] proposed a user authentication method based on KD and mouse dynamics involving comparison of all of the representative time windows and dimensionality-reduction targets of the KD features to determine the parameters for ensuring the robustness of the algorithm and, using real-world setting, the HCI dataset achieved 89.22% accuracy in authenticating users, thus demonstrating the effectiveness of the algorithm. X. Wang, Shi, Zheng, Zhang, Hong, and Cao [44] presented a user authentication method that relies on scene-related and user-related features for user identification: first, features are extracted based on keystroke and mouse movement data; next, scene-related features are obtained that have a low correlation with scenes; lastly, scene-related and user-related features are fused to ensure their integrity.…”

Section: Behavioral Authentication Using MLmentioning

confidence: 99%

A Secure and Scalable Behavioral Dynamics Authentication Model

Oladipo,

Awotunde,

Nicho

et al. 2023

IJACSA

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Various authentication methods have been proposed to mitigate data breaches. However, the increasing frequency of data breaches and users' lack of awareness have exposed traditional methods, including single-factor passwordbased systems and, two-factor authentication systems, to vulnerabilities against attacks. While behavioral authentication holds promise in tackling these issues, it faces challenges concerning interoperability between operating systems, the security of behavioral data, accuracy enhancement, scalability, and cost. This research presents a scalable dynamic behavioral authentication model utilizing keystroke typing patterns. The model is constructed around five key components: humancomputer interface devices, encryption of behavioral data, consideration of the authenticator's emotional state, incorporation of cross-platform features, and proposed implementation solutions. It addresses potential typing errors and employs data encryption for behavioral data, achieving a harmonious blend of usability and security by leveraging keyboard dynamics. This is accomplished through the implementation of a web-based authentication system that integrates Convolutional Neural Networks (CNNs) for advanced feature engineering. Keystroke typing patterns were gathered from participants and subsequently employed to evaluate the system's keystroke timing verification, login ID verification, and error handling capabilities. The web-based system uniquely identifies users by merging their username-password (UN-PW) credentials with their keyboard typing patterns, all while securely storing the keystroke data. Given the achievement of a 100% accuracy rate, the proposed Behavioral Dynamics Authentication Model (BDA) introduces future researchers to five scalable constructs. These constructs offer an optimal combination, tailored to the device and context, for maximizing effectiveness. This achievement underscores its potential applications in the realm of authentication.

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Section: Behavioral Authentication Using MLmentioning

confidence: 99%

A Secure and Scalable Behavioral Dynamics Authentication Model

Oladipo,

Awotunde,

Nicho

et al. 2023

IJACSA

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Enhancing keystroke dynamics accuracy with optimal SVM kernel usage

Yaacob,

Idrus,

Mohammed

2024

AIP Conference Proceedings

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Mouse Dynamics Behavioral Biometrics: A Survey

Khan,

Devlen,

Manno

et al. 2024

ACM Comput. Surv.

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Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical user interface (GUI)) and widget interactions (another modality closely related to mouse dynamics that also considers the target (widget) of a GUI interaction, such as links, buttons, and combo-boxes) can bolster the security of existing authentication systems because of their ability to distinguish an individual based on their unique features. As a result, it can be difficult for an imposter to impersonate these behavioral biometrics, making them suitable for authentication. In this paper, we survey the literature on mouse dynamics and widget interactions dated from 1897 to 2023. We begin our survey with an account of the psychological perspectives on behavioral biometrics. We then analyze the literature along the following dimensions: tasks and experimental settings for data collection, taxonomy of raw attributes, feature extractions and mathematical definitions, publicly available datasets, algorithms (statistical, machine learning, and deep learning), data fusion, performance, and limitations. Lastly, we end the paper with presenting challenges and promising research opportunities.

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User authentication method based on keystroke dynamics and mouse dynamics using HDA

Cited by 3 publications

References 42 publications

A Secure and Scalable Behavioral Dynamics Authentication Model

A Secure and Scalable Behavioral Dynamics Authentication Model

Enhancing keystroke dynamics accuracy with optimal SVM kernel usage

Mouse Dynamics Behavioral Biometrics: A Survey

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